alias sadn and ssdn, port ncc

Project.toml

@@ -6,7 +6,9 @@ version = "0.2.4"
 ColorVectorSpace = "c3611d14-8923-5661-9e6a-0046d554d3a4"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 Distances = "0.8.1"

src/ImageDistances.jl

@@ -1,7 +1,7 @@
 module ImageDistances
 
 using MappedArrays
-using Distances
+using Distances, LinearAlgebra, Statistics
 using Distances:
     get_common_ncols,
     get_colwise_dims,
@@ -68,6 +68,7 @@ export
     MeanAbsoluteError,
     MeanSquaredError,
     RootMeanSquaredError,
+    NCC,
 
     # helper functions
     hausdorff,
@@ -77,7 +78,8 @@ export
     ssd,
     mae,
     mse,
-    rmse
+    rmse,
+    ncc
 
 """
 `ImageDistances` is an image-related distance package built based on `Distances`.

src/metrics.jl

@@ -21,19 +21,37 @@ struct SumSquaredDifference <: Metric end
 @doc raw"""
     MeanAbsoluteError <: Metric
     mae(x, y)
+    sadn(x, y)
 
 mean absolute error(mae) is calculated by [`sad`](@ref)`(x, y)/length(x)`
 """
 struct MeanAbsoluteError <: Metric end
 
 @doc raw"""
     MeanSquaredError <: Metric
-    mse(x, y) <: Metric
+    mse(x, y)
+    ssdn(x, y)
 
 mean squared error(mse) is calculated by [`ssd`](@ref)`(x, y)/length(x)`
 """
 struct MeanSquaredError <: Metric end
 
+@doc raw"""
+    NCC <: Metric
+    ncc(x, y)
+
+Normalized Cross-correlation(ncc) is calculated by `dot(mean(x),mean(y))/(norm(mean(x))*norm(mean(y)))`
+
+```math
+\frac{<\bar{x}, \bar{y}>}{||\bar{x}||*||\bar{y}||}
+```
+
+!!! info
+
+    NCC isn't a Metric because `ncc(x, x) == 1.0`
+"""
+struct NCC <: PreMetric end
+
 @doc raw"""
     RootMeanSquaredError <: Metric
     rmse(x, y)
@@ -64,6 +82,8 @@ ssd(a::GenericImage, b::GenericImage) = SumSquaredDifference()(a, b)
 
 @doc (@doc MeanAbsoluteError)
 mae(a::GenericImage, b::GenericImage) = MeanAbsoluteError()(a, b)
+@doc (@doc MeanAbsoluteError)
+const sadn = mae
 
 
 # MeanSquaredError
@@ -72,6 +92,8 @@ mae(a::GenericImage, b::GenericImage) = MeanAbsoluteError()(a, b)
 
 @doc (@doc MeanSquaredError)
 mse(a::GenericImage, b::GenericImage) = MeanSquaredError()(a, b)
+@doc (@doc MeanSquaredError)
+const ssdn = mse
 
 
 # RootMeanSquaredError
@@ -80,3 +102,30 @@ mse(a::GenericImage, b::GenericImage) = MeanSquaredError()(a, b)
 
 @doc (@doc RootMeanSquaredError)
 rmse(a::GenericImage, b::GenericImage) = RootMeanSquaredError()(a, b)
+
+# NCC
+function (::NCC)(a::GenericGrayImage, b::GenericGrayImage)
+    A = channelview(of_eltype(floattype(eltype(a)), a))
+    B = channelview(of_eltype(floattype(eltype(b)), b))
+    return _ncc(A, B)
+end
+
+function (::NCC)(a::AbstractArray{<:Color3}, b::AbstractArray{<:Color3})
+    A = of_eltype(floattype(eltype(a)), a)
+    B = of_eltype(floattype(eltype(b)), b)
+    return _ncc(A, B)
+end
+
+function _ncc(A::GenericImage, B::GenericImage)
+    Am = (A.-mean(A))[:]
+    Bm = (B.-mean(B))[:]
+    return _dot(Am,Bm)/(norm(Am)*norm(Bm))
+end
+
+_dot(a::AbstractArray{<:Number}, b::AbstractArray{<:Number}) =
+    dot(a, b)
+_dot(a::GenericImage, b::GenericImage) =
+    mapreduce(xy->dotc(xy...), +, zip(a, b))
+
+@doc (@doc NCC)
+ncc(a::GenericImage, b::GenericImage) = NCC()(a, b)

test/metrics.jl

@@ -10,6 +10,7 @@ dist_list = [SqEuclidean(),
             Minkowski(2.5),
             Hamming(),
             TotalVariation(),
+            NCC(),
             SumAbsoluteDifference(),
             SumSquaredDifference(),
             MeanAbsoluteError(),

test/references/NCC_2d_Gray.txt

@@ -0,0 +1 @@
+-1.0

test/references/NCC_2d_RGB.txt

@@ -0,0 +1 @@
+0.06333332508802414